CVE-2021-41217: TensorFlow GPU - Null Pointer Exception in Control Flow Graph Building
Summary
CVE-2021-41217: TensorFlow is an open source platform for machine learning. Affects tensorflow-gpu across 5 version ranges.
< 2.4.4
>= 2.5.0, < 2.5.2
>= 2.6.0, < 2.6.1
>= 2.7.0rc0, < 2.7.0
< 05cbebd3c6bb8f517a158b0155debb8df79017ff
Fixed in 4 releases; the minimum fixed version depends on the release line you run. How to fix ↓
Also tracked for
- tensorflow · Bitnami
- tensorflow · PyPI
- tensorflow · PyPI
- tensorflow-cpu · PyPI
The same CVE-2021-41217 is tracked separately for each ecosystem and vendor. Its fix information is the same; the package and affected versions differ.
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Background
TensorFlow is an open-source platform for machine learning. It provides a comprehensive ecosystem of tools, libraries, and resources for developing and deploying machine learning models. TensorFlow GPU is a specific package that enables GPU acceleration for TensorFlow computations. It allows users to leverage the power of GPUs to accelerate training and inference processes in machine learning models.
Vulnerability Detail
The vulnerability identified as CVE-2021-41217 affects the process of building the control flow graph for a TensorFlow model. In certain versions of TensorFlow, there is a flaw that leads to a null pointer exception when nodes that should be paired are not. The issue arises from an assumption made in the code that the first node in the pairing always exists when encountering the second node. However, when this assumption is not met, the 'parent' node becomes a null pointer, causing a crash. This vulnerability can be exploited by an attacker to cause a denial-of-service (DoS) condition or potentially execute arbitrary code on the affected system.
How To Fix
Latest published version: 2.12.0 as of 22 Oct 2026
pip install --upgrade "tensorflow-gpu==2.12.0"Upgrading to it may include breaking changes; the versions below are the minimum fixed release for this vulnerability.
If you can't move to the latest
The minimum fixed version depends on the release line you run.
-
< 2.4.42.4.4pip install --upgrade "tensorflow-gpu==2.4.4" -
>= 2.5.0, < 2.5.22.5.2pip install --upgrade "tensorflow-gpu==2.5.2" -
>= 2.6.0, < 2.6.12.6.1pip install --upgrade "tensorflow-gpu==2.6.1" -
>= 2.7.0rc0, < 2.7.02.7.0pip install --upgrade "tensorflow-gpu==2.7.0"
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Workaround
There is no specific workaround provided for this vulnerability. The recommended approach is to update TensorFlow GPU to the fixed versions mentioned above.
Conclusion
The vulnerability identified as CVE-2021-41217 in TensorFlow GPU poses a risk of a null pointer exception during the control flow graph building process. This can lead to a crash or potential execution of arbitrary code. To mitigate this vulnerability, it is crucial to update TensorFlow GPU to the fixed versions, such as 2.7.0, 2.6.1, 2.5.2, or 2.4.4. For further assistance or support in vulnerability remediation, please contact DevNack.com.
See every vulnerability recorded for tensorflow-gpu →
FAQ
TensorFlow GPU is a package that enables GPU acceleration for TensorFlow computations, allowing users to leverage the power of GPUs for faster training and inference in machine learning models.
A control flow graph represents the flow of operations and dependencies in a TensorFlow model. It is used to define the execution order of operations and track data dependencies between nodes.
This vulnerability can lead to a null pointer exception, causing a crash in the control flow graph building process. In some cases, it may allow an attacker to execute arbitrary code or cause a denial-of-service (DoS) condition on the affected system.
No, not all versions of TensorFlow GPU are affected. The vulnerability exists in versions 2.4.4 and above, including 2.5.2, 2.6.1, and 2.7.0rc0. Earlier versions are not impacted.
There is no known exploit for this vulnerability at the time of this advisory. However, it is recommended to apply the necessary updates to mitigate the risk.
References
Related vulnerabilities
Recent vulnerabilities recorded for tensorflow-gpu.
